{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a89282d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: tensorflow in c:\\programdata\\anaconda3\\lib\\site-packages (2.9.1)\n",
      "\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (3.19.4)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (0.26.0)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.20.3)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (3.2.1)\n",
      "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (58.0.4)\n",
      "Requirement already satisfied: packaging in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (21.0)\n",
      "Requirement already satisfied: flatbuffers<2,>=1.12 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.12)\n",
      "Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.10.0,>=2.9.0rc0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.0.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (14.0.1)\n",
      "Requirement already satisfied: tensorboard<2.10,>=2.9 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.46.3)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (3.10.0.2)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow) (0.37.0)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.0.2)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.6.6)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.26.0)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (3.3.7)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (4.8)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (5.2.0)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow) (4.8.1)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow) (3.6.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (3.2)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (1.26.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2021.10.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from packaging->tensorflow) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "%pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "863ab1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow \n",
    "from tensorflow import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32b35b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "048a3ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87554 entries, 0 to 87553\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 125.6 MB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('mitbih_train.csv', header=None)\n",
    "data.info()\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91727168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977941</td>\n",
       "      <td>0.926471</td>\n",
       "      <td>0.681373</td>\n",
       "      <td>0.245098</td>\n",
       "      <td>0.154412</td>\n",
       "      <td>0.191176</td>\n",
       "      <td>0.151961</td>\n",
       "      <td>0.085784</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.049020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.960114</td>\n",
       "      <td>0.863248</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.196581</td>\n",
       "      <td>0.094017</td>\n",
       "      <td>0.125356</td>\n",
       "      <td>0.099715</td>\n",
       "      <td>0.088319</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659459</td>\n",
       "      <td>0.186486</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.059459</td>\n",
       "      <td>0.056757</td>\n",
       "      <td>0.043243</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.045946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.925414</td>\n",
       "      <td>0.665746</td>\n",
       "      <td>0.541436</td>\n",
       "      <td>0.276243</td>\n",
       "      <td>0.196133</td>\n",
       "      <td>0.077348</td>\n",
       "      <td>0.071823</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>0.066298</td>\n",
       "      <td>0.058011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.967136</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830986</td>\n",
       "      <td>0.586854</td>\n",
       "      <td>0.356808</td>\n",
       "      <td>0.248826</td>\n",
       "      <td>0.145540</td>\n",
       "      <td>0.089202</td>\n",
       "      <td>0.117371</td>\n",
       "      <td>0.150235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 188 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.977941  0.926471  0.681373  0.245098  0.154412  0.191176  0.151961   \n",
       "1  0.960114  0.863248  0.461538  0.196581  0.094017  0.125356  0.099715   \n",
       "2  1.000000  0.659459  0.186486  0.070270  0.070270  0.059459  0.056757   \n",
       "3  0.925414  0.665746  0.541436  0.276243  0.196133  0.077348  0.071823   \n",
       "4  0.967136  1.000000  0.830986  0.586854  0.356808  0.248826  0.145540   \n",
       "\n",
       "        7         8         9    ...  178  179  180  181  182  183  184  185  \\\n",
       "0  0.085784  0.058824  0.049020  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.088319  0.074074  0.082621  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.043243  0.054054  0.045946  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.060773  0.066298  0.058011  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.089202  0.117371  0.150235  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   186  187  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  \n",
       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f12fe0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "183    0\n",
       "184    0\n",
       "185    0\n",
       "186    0\n",
       "187    0\n",
       "Length: 188, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc322eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='187', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df.value_counts()\n",
    "classes = []\n",
    "sns.countplot(x=187, data = df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85307c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_1 = df[df[187]==1.0]\n",
    "class_2 = df[df[187]==2.0]\n",
    "class_3 = df[df[187]==3.0]\n",
    "class_4 = df[df[187]==4.0]\n",
    "class_0 = df[df[187]==0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5713c6e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Distribution des donnees d'entrainement sur 5 classes\n",
    "new_df = pd.concat([class_0, class_1, class_2, class_3, class_4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d8406e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977941</td>\n",
       "      <td>0.926471</td>\n",
       "      <td>0.681373</td>\n",
       "      <td>0.245098</td>\n",
       "      <td>0.154412</td>\n",
       "      <td>0.191176</td>\n",
       "      <td>0.151961</td>\n",
       "      <td>0.085784</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.049020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.960114</td>\n",
       "      <td>0.863248</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.196581</td>\n",
       "      <td>0.094017</td>\n",
       "      <td>0.125356</td>\n",
       "      <td>0.099715</td>\n",
       "      <td>0.088319</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659459</td>\n",
       "      <td>0.186486</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.070270</td>\n",
       "      <td>0.059459</td>\n",
       "      <td>0.056757</td>\n",
       "      <td>0.043243</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.045946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.925414</td>\n",
       "      <td>0.665746</td>\n",
       "      <td>0.541436</td>\n",
       "      <td>0.276243</td>\n",
       "      <td>0.196133</td>\n",
       "      <td>0.077348</td>\n",
       "      <td>0.071823</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>0.066298</td>\n",
       "      <td>0.058011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.967136</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830986</td>\n",
       "      <td>0.586854</td>\n",
       "      <td>0.356808</td>\n",
       "      <td>0.248826</td>\n",
       "      <td>0.145540</td>\n",
       "      <td>0.089202</td>\n",
       "      <td>0.117371</td>\n",
       "      <td>0.150235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 188 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.977941  0.926471  0.681373  0.245098  0.154412  0.191176  0.151961   \n",
       "1  0.960114  0.863248  0.461538  0.196581  0.094017  0.125356  0.099715   \n",
       "2  1.000000  0.659459  0.186486  0.070270  0.070270  0.059459  0.056757   \n",
       "3  0.925414  0.665746  0.541436  0.276243  0.196133  0.077348  0.071823   \n",
       "4  0.967136  1.000000  0.830986  0.586854  0.356808  0.248826  0.145540   \n",
       "\n",
       "        7         8         9    ...  178  179  180  181  182  183  184  185  \\\n",
       "0  0.085784  0.058824  0.049020  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.088319  0.074074  0.082621  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.043243  0.054054  0.045946  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.060773  0.066298  0.058011  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.089202  0.117371  0.150235  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   186  187  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  \n",
       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "166a1354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87549</th>\n",
       "      <td>0.807018</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.536842</td>\n",
       "      <td>0.529825</td>\n",
       "      <td>0.491228</td>\n",
       "      <td>0.484211</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>0.396491</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>0.136842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87550</th>\n",
       "      <td>0.718333</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>0.486667</td>\n",
       "      <td>0.361667</td>\n",
       "      <td>0.231667</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.051667</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87551</th>\n",
       "      <td>0.906122</td>\n",
       "      <td>0.624490</td>\n",
       "      <td>0.595918</td>\n",
       "      <td>0.575510</td>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.481633</td>\n",
       "      <td>0.444898</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.322449</td>\n",
       "      <td>0.191837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87552</th>\n",
       "      <td>0.858228</td>\n",
       "      <td>0.645570</td>\n",
       "      <td>0.845570</td>\n",
       "      <td>0.248101</td>\n",
       "      <td>0.167089</td>\n",
       "      <td>0.131646</td>\n",
       "      <td>0.121519</td>\n",
       "      <td>0.121519</td>\n",
       "      <td>0.118987</td>\n",
       "      <td>0.103797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87553</th>\n",
       "      <td>0.901506</td>\n",
       "      <td>0.845886</td>\n",
       "      <td>0.800695</td>\n",
       "      <td>0.748552</td>\n",
       "      <td>0.687138</td>\n",
       "      <td>0.599073</td>\n",
       "      <td>0.512167</td>\n",
       "      <td>0.427578</td>\n",
       "      <td>0.395133</td>\n",
       "      <td>0.402086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 188 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6    \\\n",
       "87549  0.807018  0.494737  0.536842  0.529825  0.491228  0.484211  0.456140   \n",
       "87550  0.718333  0.605000  0.486667  0.361667  0.231667  0.120000  0.051667   \n",
       "87551  0.906122  0.624490  0.595918  0.575510  0.530612  0.481633  0.444898   \n",
       "87552  0.858228  0.645570  0.845570  0.248101  0.167089  0.131646  0.121519   \n",
       "87553  0.901506  0.845886  0.800695  0.748552  0.687138  0.599073  0.512167   \n",
       "\n",
       "            7         8         9    ...  178  179  180  181  182  183  184  \\\n",
       "87549  0.396491  0.284211  0.136842  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "87550  0.001667  0.000000  0.013333  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "87551  0.387755  0.322449  0.191837  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "87552  0.121519  0.118987  0.103797  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "87553  0.427578  0.395133  0.402086  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       185  186  187  \n",
       "87549  0.0  0.0  4.0  \n",
       "87550  0.0  0.0  4.0  \n",
       "87551  0.0  0.0  4.0  \n",
       "87552  0.0  0.0  4.0  \n",
       "87553  0.0  0.0  4.0  \n",
       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1d7e1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='187', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotter la distribution des données\n",
    "sns.countplot(x=187, data = new_df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2974d7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 0\n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 5, figsize=(25,6))\n",
    "\n",
    "for i in range(5):\n",
    "  ax[i].plot(new_df[new_df[187]==float(i)].sample(1).iloc[0,:186])\n",
    "  ax[i].set_title('Class: '+str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3358ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Diviser la base de données en test et train\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_df.drop([187], axis=1), new_df[187], test_size = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7159294",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X_train).reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test = np.array(X_test).reshape(X_test.shape[0], X_test.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ecd5737",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "df_1=data[data[187]==1]\n",
    "df_2=data[data[187]==2]\n",
    "df_3=data[data[187]==3]\n",
    "df_4=data[data[187]==4]\n",
    "df_0=(data[data[187]==0]).sample(n=20000,random_state=42)\n",
    "\n",
    "\n",
    "df_1_upsample=resample(df_1,replace=True,n_samples=20000,random_state=1)\n",
    "df_2_upsample=resample(df_2,replace=True,n_samples=20000,random_state=2)\n",
    "df_3_upsample=resample(df_3,replace=True,n_samples=20000,random_state=3)\n",
    "df_4_upsample=resample(df_4,replace=True,n_samples=20000,random_state=4)\n",
    "\n",
    "mit_train_data=pd.concat([df_0,df_1_upsample,df_2_upsample,df_3_upsample,df_4_upsample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7440feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train=keras.utils.np_utils.to_categorical(y_train)\n",
    "y_test=keras.utils.np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1079bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential,utils\n",
    "from tensorflow.keras.layers import Flatten, Dense, Conv1D, MaxPool1D, Dropout , Conv2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c482240f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 187, 32)           128       \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 187, 64)           6208      \n",
      "                                                                 \n",
      " conv1d_2 (Conv1D)           (None, 187, 128)          41088     \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1D  (None, 94, 128)          0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 94, 128)           0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 12032)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               6160896   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1024)              525312    \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 5)                 5125      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,738,757\n",
      "Trainable params: 6,738,757\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(filters=32, kernel_size=(3,), padding='same', activation='relu', input_shape = (X_train.shape[1],1)))\n",
    "model.add(Conv1D(filters=64, kernel_size=(3,), padding='same', activation='relu')) \n",
    "model.add(Conv1D(filters=128, kernel_size=(5,), padding='same', activation='relu'))    \n",
    "\n",
    "model.add(MaxPool1D(pool_size=(3,), strides=2, padding='same'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(units = 512, activation='relu'))\n",
    "model.add(Dense(units = 1024, activation='relu'))\n",
    "\n",
    "model.add(Dense(units = 5, activation='softmax'))\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4fe8d544",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [EarlyStopping(monitor='val_loss', patience=8),\n",
    "             ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf0f5bf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "2463/2463 [==============================] - 185s 75ms/step - loss: 0.0320 - accuracy: 0.9898 - val_loss: 0.0560 - val_accuracy: 0.9860\n",
      "Epoch 2/20\n",
      "2463/2463 [==============================] - 183s 74ms/step - loss: 0.0284 - accuracy: 0.9909 - val_loss: 0.0686 - val_accuracy: 0.9845\n",
      "Epoch 3/20\n",
      "2463/2463 [==============================] - 181s 73ms/step - loss: 0.0261 - accuracy: 0.9918 - val_loss: 0.0685 - val_accuracy: 0.9857\n",
      "Epoch 4/20\n",
      "2463/2463 [==============================] - 185s 75ms/step - loss: 0.0258 - accuracy: 0.9921 - val_loss: 0.0764 - val_accuracy: 0.9849\n",
      "Epoch 5/20\n",
      "2463/2463 [==============================] - 182s 74ms/step - loss: 0.0238 - accuracy: 0.9928 - val_loss: 0.0613 - val_accuracy: 0.9857\n",
      "Epoch 6/20\n",
      "2463/2463 [==============================] - 185s 75ms/step - loss: 0.0220 - accuracy: 0.9930 - val_loss: 0.0704 - val_accuracy: 0.9848\n",
      "Epoch 7/20\n",
      "2463/2463 [==============================] - 185s 75ms/step - loss: 0.0212 - accuracy: 0.9935 - val_loss: 0.0694 - val_accuracy: 0.9847\n",
      "Epoch 8/20\n",
      "2463/2463 [==============================] - 189s 77ms/step - loss: 0.0201 - accuracy: 0.9936 - val_loss: 0.0700 - val_accuracy: 0.9853\n",
      "Epoch 9/20\n",
      "2463/2463 [==============================] - 192s 78ms/step - loss: 0.0210 - accuracy: 0.9937 - val_loss: 0.0752 - val_accuracy: 0.9863\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20 , callbacks=callbacks, batch_size=32)\n",
    "model.load_weights('best_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58a66d53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274/274 [==============================] - 4s 16ms/step\n"
     ]
    }
   ],
   "source": [
    "# Prédiction\n",
    "\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "786ac64f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14d3bb566d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0)\n",
    "#Accuracy vs epochs\n",
    "plt.plot(history.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "#Loss vs epochs\n",
    "plt.figure(1)\n",
    "plt.plot(history.history['loss'], label='training loss')\n",
    "plt.plot(history.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c008373",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274/274 [==============================] - 5s 17ms/step - loss: 0.0560 - accuracy: 0.9860\n",
      "Test loss: 0.056032199412584305\n",
      "Test accuracy: 0.9859524965286255\n"
     ]
    }
   ],
   "source": [
    "#Calculer l'accuracy du modèle\n",
    "loss,accuracy=model.evaluate(X_test,y_test)\n",
    "\n",
    "print('Test loss:', loss)\n",
    "print('Test accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a24c0f25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274/274 [==============================] - 4s 16ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dessiner la matrice de confusion\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "Y_pred = model.predict(X_test)\n",
    "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
    "Y_true = np.argmax(y_test,axis = 1) \n",
    "\n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "\n",
    "f,ax = plt.subplots(figsize=(8, 8))\n",
    "sns.heatmap(confusion_mtx, annot=True, linewidths=0.01,cmap=\"Oranges\",linecolor=\"gray\", fmt= '.1f',ax=ax)\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c896c03f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      7224\n",
      "           1       0.86      0.81      0.84       236\n",
      "           2       0.98      0.96      0.97       582\n",
      "           3       0.88      0.75      0.81        68\n",
      "           4       0.99      1.00      0.99       646\n",
      "\n",
      "    accuracy                           0.99      8756\n",
      "   macro avg       0.94      0.90      0.92      8756\n",
      "weighted avg       0.99      0.99      0.99      8756\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(Y_true, Y_pred_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50edcff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72f0a3fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4abff8f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
